Communication content tailoring

ABSTRACT

A method for personalizing a message between a sender and a receiver is provided. The method comprises semantically analyzing a communication history to form a knowledge graph, deriving formality level values using a first trained ML model, analyzing parameter values of replies to determine receiver impact score, and training a second ML system to generate a model to predict the receiver impact score value. The method also comprises selecting a linguistic expression in a message being drafted, determining an expression intent, modifying the linguistic expression based on the formality level and the expression intent to generate a modified linguistic expression, and testing whether the modified linguistic expression has an increased likelihood of a higher receiver impact score. The method also comprises repeating selecting the linguistic expression, determining the expression intent, modifying the linguistic expression, and testing until a stop criterion is met.

BACKGROUND

The disclosure relates generally to a method for personalizing amessage, and more specifically, to a method for personalizing a messageto be sent from a sender to a receiver. The disclosure relates furtherto a message personalizing system and computer program product forpersonalizing a message between a sender and a receiver.

The amount of data managed in electronic communication systems isever-increasing. This is true for classic email messages, chat messages,social media messages, and other forms of modern communicationplatforms. It has also become common to use more informal electroniccommunication mechanisms, such as voicemail, in business correspondenceand marketing. The number of messages being sent in any of theabove-mentioned ways is also increasing and, in many cases, the lengthof a single message is decreasing. This may result in misunderstandingsand miscommunications using electronic media. Additionally, a messagetailored for one recipient may “hit the nail on the head” in the contextbetween the sender and one recipient but may be completely content-freein the context between the sender and another recipient. Therefore,messages should be tailored to both the requirements and the context ofa recipient in order to achieve the proper and maximum impact.

However, many messages are sent by a non-concentrated sender.Alternatively, marketing messages may be sent out to a large audience inwhich each receiving individual has a unique history and thus differentcontexts when receiving the message. Hence, the impact for the pluralityof receivers may vary significantly. In order to address this problem,prior to marketing campaigns, highly complex sentiment analyses areoften performed in order to send context-specific messages to individualreceivers or small groups of receivers. These sentiment analyses are amajor effort if electronic communication systems and platforms are usedfor marketing. Additionally, the same weakness can be observed in many1:1 messages because the sender does not know the complete context ofthe receiver or how the receiver may interpret the message contentbecause it was necessarily generated in the context of the sender.

Some attempts have already been made to address these problems. U.S.Patent Publication 2012/0265528 A1 discloses a virtual assistant whichuses context information to supplement natural language or gesturalcheck input from a user. The context helps to clarify the user's intentto reduce the number of candidate interpretations of the user's input,and reduces the need for the user to provide excessive clarificationinput.

Furthermore, U.S. Patent Publication 2013/0253910 A1 discloses a methodfor analyzing text within a digital document. In some cases the analysiscan include receiving and/or generating a digital document withprocessing circuitry and determining a distribution of each of aplurality of document terms based on occurrences of the document termswithin the text sample.

However, the known technologies do not take into consideration thecomplete context of a recipient and therefore may not appropriatelytailor the message to decrease the probability for misinterpretation bythe recipient. Therefore, there is a need for better personalization ofmessages.

SUMMARY

According to one aspect of the present disclosure, a method forpersonalizing a message between a sender and a receiver may be provided.The method may comprise semantically analyzing a communication historybetween the sender and the receiver and forming a knowledge graphbetween a sender identifier identifying the sender and a receiveridentifier identifying the receiver. Furthermore, the method maycomprise deriving from the knowledge graph formality level valuesbetween the sender and the receiver, using a first trainedmachine-learning model, analyzing parameter values of replies in thecommunication history to determine receiver impact score values andtraining a second machine-learning system to generate a model to predictthe receiver impact score value based on the knowledge graph and theformality level.

Additionally, the method may comprise selecting a linguistic expressionin a message being drafted, determining an expression intent of theselected linguistic expression, and modifying the linguistic expressionbased on the formality level and the expression intent, therebygenerating a modified linguistic expression.

Also part of the method may be testing if the modified linguisticexpression has an increased likelihood to lead to a higher receiverimpact score value using a third trained machine-learning model andrepeating the steps of selecting the linguistic expression, determiningthe expression intent, modifying the linguistic expression and testinguntil a stop criterion is met.

According to another aspect of the present disclosure, a messagepersonalizing system for personalizing a message between a sender and areceiver may be provided. The message personalizing system may comprisefirst analysis means adapted for semantically analyzing a communicationhistory between the sender and the receiver, and adapted for forming aknowledge graph between a sender identifier identifying the sender and areceiver identifier identifying the receiver, deriving means adapted forderiving from the knowledge graph formality level values between thesender and the receiver, using a first trained machine-learning model,second analysis means adapted for analyzing parameter values of repliesin the communication history to determine receiver impact score values,and training means adapted for training a second machine-learning systemto generate a model to predict the receiver impact score value based onthe knowledge graph and the formality level.

Moreover, the message personalizing system may comprise selection meansadapted for selecting a linguistic expression in a message beingdrafted, determination means adapted for determining an expressionintent of the selected linguistic expression, modification means adaptedfor modifying the linguistic expression based on the formality level andthe expression intent, thereby generating a modified linguisticexpression and test means adapted for testing if the modified linguisticexpression has an increased likelihood to lead to a higher receiverimpact score value using a third trained machine-learning model.

Moreover, the message personalizing system may comprise repetition meansadapted for triggering the selection means, the determination means,modification means and the test means until a stop criterion is met.

The proposed method for personalizing a message between a sender and areceiver may offer multiple advantages, technical effects, contributionsand/or improvements.

The proposed concept may represent a tool to help “bring across amessage” in the most effective way. It may overcome the old sayingaccording to which a message should be packaged seven times in adifferent way in order to really get the message across. The systemproposed here may be a shortcut to the communication rule of thumb justmentioned. A message to be drafted (and to be sent) may be modified byan interaction of the plurality of machine learning systems before themessage will be sent actively.

In order to achieve this, the proposed method and the related system mayexamine and analyze historic communication fragments between a senderand a related receiver or recipient in order to determine differentparameters, like a topic, an understanding of it, a specific topic, usedvocabulary, a relationship between the sender and the receiver and otheravailable context and metadata. Based on these experienced data amessage to be written may also be dynamically analyzed online before themessage may be sent in order to predict what kind of impact—e.g., whatkind of effect—the message formulated in a specific way may have on therecipient. Also this may depend on the plurality of differentparameters, all of which may be used if known.

Hence, the proposed concept may help to avoid misunderstandings andtime-consuming back and forth questions in order to achieve real, fastand effective progress in an ongoing communication. In particular, itmay also allow cultural, linguistic, and individual barriers to beovercome in the quest to make communication smoother and more effective.

Thereby, the proposed concept may not only work in a one-to-onecommunication but also in a one-to-many communication because themessages may be tailored to an individual recipient or also to a groupof recipients having, e.g., a comparable communication history. Thetailoring of the message may also be based on other parameters, likeselected member group characteristics.

The pure technical advantage may be seen in the fact that fewer messagesmay have to be stored in electronic communication systems of any kindsuch that less storage capacity may be needed in total. Additionally,communication bandwidth may be saved because fewer messages are sentback and forth in order to answer back questions.

In the following, additional embodiments—applicable to the method aswell as to the system—will be described.

According to an advantageous embodiment of the method, the thirdmachine-learning model may be a reinforcement learning model, e.g.,based on a reinforcement learning system. This way, the involvedsoftware agent may act in a way to maximize some notion of reward, e.g.,a reward function, and in this special case, the expected impact of themodified message to the receiver.

According to an enhanced embodiment of the method, the training of thethird machine-learning model may also comprise using a bi-directionaltransformer. This may also be seen as a forth machine-learning model andsystem involved in the method which is here based on the BERT(Bidirectional Encoder Representations from Transformers) model topredict the expression intent. Hence, the here proposed method may makeuse of the newest text interpretation and NLP techniques using aunsupervised ML model for language representations which is pre-trainedusing only a plain text corpus. Thereby, BERT may take into account thecontext for each occurrence of a given word or phrase. For instance,whereas the vector for “running” may have the same word2vec vectorrepresentation for both of its occurrences in the sentences “He isrunning a company” and “He is running a marathon”, BERT may provide acontextualized embedding that will be different according to thesentence.

According to another embodiment of the method, the training of thebi-directional transformer may comprise using as training data thecommunication history, the knowledge graph, the derived formality levelsand receiver impact score values. Hence, the full spectrum of availableand historically given, derived and constructed (e.g., in the knowledgegraph) data and dependencies between selected ones of the data pointsmay be made available for the bi-directional transformer, e.g., the BERTmodel. This may allow a more or less complete set of input data foroptimizing the final message formulation to the receiver for a maximumof impact.

According to a further developed embodiment of the method, the receiveridentifier may be a plurality of receiver identifiers used to identify aplurality of users, and the modified linguistic expression may be builttaking effects of all receiver identifiers into account. Hence, theproposed concept may not only be advantageous in a 1:1 communicationtask but also in a 1-to-many communication task. Thereby, it may beuseful to build average values for data relating to a plurality oftarget identifiers and/or also using weighing function, e.g., based onformality level values, weighing on topics, e.g., using a look-up tablefor weights and topics and potentially, e.g., receiver IDs. It may alsobe possible to create different versions of the modified message fordifferent receivers or groups of receivers. The last aspect may beparticularly interesting in mass communication and social mediaadvertising.

According to an advance embodiment of the method, the modification ofthe linguistic expression may be influenced—e.g., it's modifiedversion—by outcomes of at least one selected out of the formality levelanalysis, a confidentiality level analysis of the message, a messagetopic analysis, and atone analysis, which may be identified to befriendly, aggressive, or cooperative, etc. The influence may be based onan influence function the result of which are the analysis result andthe analysis' outcomes may be used as arguments of the function.

According to one further advanced embodiment of the method, themodification of the linguistic expression may be performed by at leastone selected out of a replacement of a word, spinning of sentencestructure, reordering of content, deletion of a word, re-shuffle anorder of message blocks, use of synonyms, include wording which was usedby the receiver in past communication, adjust the style—in particular,guiding the style adjustments using the personality insights profilefrom the receiver—and a GPT2 (generative pre-training mode based on OpenAI) transformer transformation, wherein the GPT2 transformertransformation is inculcated with partial sentence creation to generatethe rest of the paragraph in a personalized manner by synthesizingpersonalized text for the user. Hence, all or most aspects of amodification of the message may be addressed which would also be used bya consciousness and full focused user. However, many messages may onlybe typed without the full attention of the sender. However, the hereproposed system using this here-described feature may increase theeffect the sent message may unfold.

According to an interesting embodiment of the method, the semanticanalysis of the communication history may comprise identifying topicswith the latent Dirichlet allocation (LDA) topic model. Thereby, theadvantages of the LDA topic model may be incorporated into the hereproposed concept, namely, through the ability of LDA to model documentsthrough the process of receiving a number of topics by a user, andidentifying topics based on the multi-nominal distributions of theDirichlet distribution of terms in a document collection.

Hence, the proposed method may also easily work in environments in whichthe communication history did not only circle around a discussion topicbut which comprises a plurality of different topics.

According to a permissive embodiment of the method, the formality levelmay be obtained with a bag-of-words model and the first trainedmachine-learning model may be a Gaussian Naïve Bayes classifier.Thereby, the bag-of-words model is a simplifying representation oftenused in natural language processing and information retrieval. In thismodel, a text (such as a sentence or a document) may be represented asthe bag (multiset) of its words, disregarding grammar and even wordorders but keeping multiplicity. Additionally, the usage of the GaussianNaïve Bayes classifier may be an option to use a comparably simpleprobabilistic classifier based on applying Bayes' theorem with strong(naïve) independence assumptions between the features. This may help tokeep the computer-implemented version of the proposed concept efficientand not too resource hungry.

According to a useful embodiment of the method, the linguisticexpression may further be refined by predicting the receiver's mood ortasks at the time at which the message is set to arrive using IoT(Internet-of-Things) sensors and/or calendar information. This may beimplemented by fetching sensor specific instructions and storing theretrieved information in a private (cloud) repository for running themodel. Thus, the method may also use additional data sources over andabove the usage of the communication history but also the expectedexperience world of the receiver.

According to another advanced embodiment of the method, the receiverimpact score may be influenced by—in the sense of being a function of—atleast one selected out of IoT sensor data, wearable system data,computer vision data, presence/absence of a reply, timing of a reply,timing of a reply in relation to other replies by the same recipients ordifferent recipients, timing of a reply in relation to time zones andcalendars, presence/absence of out-of-office messages, length of thereply, content of the reply, a task requested by the sender beingcarried out by the receiver—and e.g., confirming this via a reply—and anemoji (e.g., ideograms and/or smiley-like character combinations used inelectronic messages).

This way, the receiver's personal environmental data like hisheartbeat/blood-pressure data, specific body movements, facialexpressions, environmental parameter values (e.g., temperature,humidity, atmospheric, pressure, etc. may be used to better understandthe impact of a given message to be sent.

According to a further optional embodiment of the method, the messagemay be a written message, like an email or a chat comment, or a voicemessage. For this, also the voice communication history may be used aspart of the historic communication data.

According to a another optional embodiment of the method, in case theanalysis may result in the fact that no communication history exists, acommunication history between the receiver and another sender replacesthe communication history between the sender and the receiver.Therefore, the proposed concept may also be applicable between a senderand a receiver which do not have a communication history, hence a newtarget audience. This may particularly helpful in marketing email burstsin which many new recipients may be addressed.

Furthermore, embodiments may take the form of a related computer programproduct, accessible from a computer-usable or computer-readable mediumproviding program code for use, by, or in connection, with a computer orany instruction execution system. For the purpose of this description, acomputer-usable or computer-readable medium may be any apparatus thatmay contain means for storing, communicating, propagating ortransporting the program for use, by, or in connection, with theinstruction execution system, apparatus, or device.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects of the present disclosure are apparent from the examplesof embodiments to be described hereinafter and are explained withreference to the examples of embodiments. One skilled in the art willrecognize alterations and modifications; thus, the disclosure is notlimited to the disclosed embodiments. Embodiments of the disclosure willbe described by way of example only and with reference to the followingdrawings.

FIG. 1 shows a block diagram of an embodiment of the method forpersonalizing a message between a sender and a receiver.

FIG. 2 shows a block diagram of an overview of components instrumentalfor the method for personalizing a message between a sender and areceiver.

FIG. 3 shows an exemplary flow chart diagram of preparatory steps forthe method for personalizing a message between a sender and a receiver.

FIG. 4 shows an exemplary diagram of a portion of a knowledge graph.

FIG. 5 shows a diagram of options to be used during an analysis phasefor the engagement detection.

FIG. 6 shows a block diagram of an embodiment of the messagepersonalizing system for personalizing a message between the sender andthe receiver.

FIG. 7 shows a block diagram of an embodiment of a computing systemcomprising the message personalizing system according to FIG. 6 .

FIG. 8 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 9 depicts abstraction model layers according to an embodiment ofthe present invention.

While the embodiments described herein are amenable to variousmodifications and alternative forms, specifics thereof have been shownby way of example in the drawings and will be described in detail. Itshould be understood, however, that the particular embodiments describedare not to be taken in a limiting sense. On the contrary, the intentionis to cover all modifications, equivalents, and alternatives fallingwithin the spirit and scope of the disclosure.

DETAILED DESCRIPTION

It should be noted that embodiments of the disclosure are described withreference to different subject-matters. In particular, some embodimentsare described with reference to method type claims, whereas otherembodiments are described with reference to apparatus type claims.However, a person skilled in the art will gather from the above and thefollowing description that, unless otherwise notified, in addition toany combination of features belonging to one type of subject-matter,also any combination between features relating to differentsubject-matters, in particular between features of the method typeclaims and features of the apparatus type claims, is considered as to bedisclosed within this document.

In the context of this description, the following conventions, termsand/or expressions may be used.

The term “communication history” may denote, in a simple case, sent andreceived emails between a sender and a receiver regarding one topic. Inmore complex cases, the communication history may relate to a group ofemails relating to a plurality of topics between the sender and thereceiver or even to all emails across multiple email systems between thesender and the receiver. Additionally, emails to other recipients ofboth the sender and the receiver may be taken into account. Moreover,other forms of electronic communication channels, particularly chatsystems or other collaboration tools, may provide data for thecommunication history. Furthermore, voice and/or video messages may beused for the communication history.

The term “sender” may denote an originator of a message to be draftedand/or sent to a designated recipient. In the simple case of an emailsystem, the designated recipient is the reader of an email message sentto an email address. The sender may also send messages using othercommunication systems.

The term “receiver” or “recipient” may denote an individual reading anemail sent by a sender which is addressed to an email address affiliatedwith the same individual reading the email. A receiver may receivemessages via other electronic communication channels.

The term “knowledge graph” may denote a data structure comprising nodesand edges. The nodes typically store facts, and edges selectively linknodes. Edges may store information on relationships between facts; asfacts are typically stored in nodes, edges may store information aboutrelationships between nodes and between facts and nodes. The informationabout fact relationships may also comprise weighing factors.

The term “sender identifier,” in the simple case of a neural system, maydenote the email address of the sender. The same may apply to areceiver/recipient and a related receiver identifier: the term “receiveridentifier” and “recipient identifier” may denote the email address ofthe receiver/recipient in the simple case of a neural system.

The term “formality level value” may denote values describing a formalrelationship which may exist between a sender person and a receiverperson. Formality level values are typically numeric; they may beexpressed as alphanumeric strings. The relationship between a senderperson and a receiver person may be described as a friendship, anemployee/employer relationship, an authority/citizen relationship, andso on. The formality level value may have an influence on the way amessage may be formulated using an electronic communication system.

The term “first trained machine-learning model” may denote a trainedmodel to derive—in particular, to classify—the formality level valuebased on the communication history. A first trained machine-learningmodel may specifically be trained to classify the formality level valuebased on communication history data. The underlying machine learningsystem may be a classification system. The underlying machine learningsystem may be based on a supervised learning approach.

The term “parameter value of a reply” may denote a dedicated analysisvalue characterizing and relating directly to an impact score value.Typically, higher parameter values of a reply may denote a higher impactby a received message with respect to the recipient. The impact may bemeasured in different ways such as speed of response, the decisivenessof the response, and the tone of the response action triggered, amongother ways. The response may include an email sent to another person, aphone call made, a web page opened, and so on.

The term “receiver impact score value” may denote a measurable valueexpressing the impact of a received message. A receiver impact scorevalue may be derived from parameter values of replies.

The term “second machine-learning system” may denote a trained machinelearning model for predicting an impact score value to a recipient of anewly drafted or modified message based on the available information.Available information may include the knowledge graph and the derivedformality level between the sender and the receiver. A secondmachine-learning system may use a supervised learning approach.

The term “linguistic expression” may denote a phrase in a longer messagecomprising one or more sentences. In particular, a linguistic expressionmay be one, two, or a few directly related words.

The term “expression intent” may denote the intention, purpose, or aimof content of a specific linguistic expression within a message.

The term “modifying,” with respect to modifying a message to be drafted,may denote that one or more linguistic expressions of the message to besent may be changed, adapted, or amended without altering the expressionintent. If more than one linguistic expression would be modified withinthe limits of the expression intent, then the overall intent of themessage to be drafted shall also not be modified.

The term “third trained machine-learning model” may denote anothermachine learning system that predicts if a modified linguisticexpression may lead to a higher impact score value of a modifiedlinguistic expression or a modified message. If that is the case, themodified message may be sent out from the sender; if that is not thecase, the modified message may be prevented from being sent out from thesender. A third trained machine-learning model may use a related machinelearning model to predict changes in impact score values as linguisticexpression is modified.

The term “reinforcement learning” and modeling based on reinforcementlearning may denote an area of machine learning concerned with how asoftware agent may take actions in an environment in order to maximizethe notion of a cumulative reward. Wherein the first two basic machinelearning paradigms are supervised learning and unsupervised learning,reinforcement learning is the third basic machine learning paradigm.Reinforcement learning is currently the most frequently used paradigm.Basically, the underlying algorithms focus on finding a balance betweenthe exploration of uncharted territory and the exploitation of currentknowledge. In one embodiment, the uncharted territory may be themodified linguistic expression, and the current knowledge may beinformation derived from communication history.

The term “bi-directional transformer” may denote a system or techniquefor natural language processing based on bidirectional encoderrepresentations from transformers (BERT). BERT was developed andpublished by Jacob Devlin et al. BERT is often used in search engines.

A detailed description of the figures is provided. All instructions inthe figures are schematic. The first figure presents a block diagram ofan embodiment of the method for personalizing a message between a senderand a receiver. Further embodiments of the method are then described aswell as embodiments of a system using the method. Specifically, thedescribed system is a message personalizing system for personalizing amessage between a sender and a receiver.

FIG. 1 shows a block diagram of a preferred embodiment of a method 100for personalizing a message between a sender and a receiver. Method 100may be viewed as having a first phase (operations 102-112) and a secondphase (operations 114-120). In a first phase, method 100 comprisessemantically analyzing a back and forth communication history betweenthe sender and the receiver, at operation 102. This may comprise everyavailable electronic message sent between the sender and the receiverincluding the affiliated metadata. The metadata of the electronicmessages may include the time of the messages, time periods betweenmessages, environmental data, sensor data, captured health data, and soon. Based on the semantic analysis of the communications history, method100 further comprises forming a knowledge graph, at operation 104,between a sender identifier which identifies a sender and a receiveridentifier which identifies a receiver.

Based on the knowledge graph, method 100 comprises deriving from theknowledge graph formality level values, at operation 106, between asender and a receiver. Thereby, a first trained machine-learning modelis used which is pre-trained based on all available information betweenthe two constituents. Formality level values which may be derived fromthe knowledge graph may include, expressions of respect, friendship,business relationship, dependency of relationships, workingrelationships, among other things.

Method 100 also comprises analyzing parameter values of replies, atoperation 108, in the communication history to determine receiver impactscore values at operation 110. Receiver impact score values may bevalues indicative of a reaction the message provoked from the receiver.

Method 100 also comprises training a second machine-learning system, atoperation 112, to generate a machine learning model to predict thereceiver impact score value based on the knowledge graph and theformality level. A supervised machine learning approach may be used totrain the second machine-learning system. This step may conclude thepreparatory phase (e.g., first phase) of the method 100.

A second phase of method 100 relates to specific messages between thesender to the receiver. More specifically, method 100 also comprisesselecting a linguistic expression in a message being drafted atoperation 114, determining an expression intent, at operation 116, ofthe selected linguistic expression, and modifying the linguisticexpression, at operation 118, based on the formality level and theexpression intent, thereby generating a modified linguistic expression.Hence, the same message with a different wording and a better expectedimpact may be used instead of the original draft of message. The messagebeing drafted may be drafted by a sender. An expression intent may bedetermined using the BERT model.

Method 100 further comprises testing of a modified linguistic expressionat operation 120. Testing, at operation 120, checks to see if a modifiedlinguistic expression an increased likelihood to lead to a higherreceiver impact score value using a trained machine-learning model.Testing, at operation 120, may be done using a reinforcement machinelearning model.

To address a longer message, method 100 may also comprise a repeating122 of the second phase (operations 114-120) of method 100 until a stopcriterion is met. An example for such a stop criterion may be that nosignificant changes in the value of the receiver impact score aremeasured from two successive simulations.

FIG. 2 shows a block diagram of an overview of components 200 forimplementing method 100. Elements in group 202 relate to the first phaseof method 100 and elements of group 204 relate to the second phase ofmethod 100. In short, the processing steps of the first phase use thecommunication history, a knowledge graph, and a trained supervisedmachine learning model to predict the likelihood of a message impactinga recipient. In an alternative implementation, the processing steps ofthe first phase may use social media data in lieu of communicationhistory. The second phase is the reinforcement learning model thatadapts the message to increase the impact on the receiver.

Portions of communication, such as text, can then be leveraged by areinforcement learning model agent 234 which can automaticallymanipulate the original message and test if adjusted versions have ahigher likelihood to lead to a more favorable action as a result of theimpact on the receiver.

Hence, the data flow is as follows: input data 206 is processed 208 tobuild a knowledge graph 210. For example, input data may be in the formof communication history such as email, chat, and/or social media. Ananalysis of the raw input data is performed. The analysis may beperformed using, for example, sentiment analysis, tone analyzer, and/orpersonal insights. This raw data plus the analysis results are theningested in the knowledge graph 210. The knowledge graph 210 may be seenas a first machine learning component.

The raw data may comprise message data 218, metadata 220, sender data222, receiver data 224, and relationship data 226. In a further enhancedimplementation, computer vision data as well as other data may beincluded. The output of this phase represents the raw and analysis data.In particular, the analysis results and the knowledge graph representthe raw and analysis data.

The raw data and analysis data are then used 212 and 216, respectively,for a next preprocessing step 214 to develop a second machine learningmodel. A supervised machine learning approach is used to enrich thealready preprocessed data with additional information. For example,formality scores may be used to enrich the preprocessed data.

Using this second machine learning model 228, matching scores arecalculated 230 and a supervised machine learning model is trained. Thistraining of the supervised machine learning model allows as an output ofpredictions of matching scores of a given portion of piece ofcommunication from the supervised machine learning model. Predictions ofmatching scores may also be denoted as engagement score on impact.

In a next step of the method, a reinforcement learning system 234iterates a message to form a tailored message. As reward function inputof the reinforcement learning 232, the following components may be used:the initial message as well as the input and output of theabove-described step. The input and output of the above-described stepmay include raw data, analysis results, knowledge graph data, formalityscores, and output of the second machine learning model to calculatematching scores. One or more components may be used for one iteration ofa reward function input of the reinforcement learning 232.

As output for one iteration, the following can be expected: the revisedmessage and its penalties and rewards. The penalties and rewards may bemeasured in the form of the matching score of the revised message.

The BERT model masking 236 is used to generate the iterations tailoredmessage. Thereby, actions on the message 238 are made to adapt it in away to be better tailored for the receiver. Approaches like wordspinner, GPT2, a transformer, et cetera may be used.

As data is used throughout the process, communication history data isextracted from the appropriate communication system(s) to train themachine learning model and to help to tailor the message. The messagedata may comprise the text of the message, entities of the text,structuring of the text, length of the text, sentiment analysis data,personal knowledge data, insider data, tone analysis data, emoticons,emoji, additional graphics, links, and other data. The structuring ofthe text may include greetings or openings, main part or text body, etcetera. The length of the text may include, for example, the measurementof the text bloc, the number of characters in words or in the messageoverall, the word count, or the number of paragraphs in thecommunication.

Meta data can also be used. Meta data may include the date and time of amessage, the type of channel, and other data that provides informationabout the communications. The type of channel may be, for example,email, chat, or phone call. The following is often seen as relevantsender data: the profile of a person, an expertise level of the personsrelated to the text, and other information which may help a sender senda tailored message. The profile of a person may include the person'sgender, age, personality insight profiles, et cetera. The expertiselevel of the persons related to the text may be based on the entityextraction.

The receiver data may comprise profile data of a person and theexpertise level of the person related to the text. The profile of aperson may include the person's gender, age, personality insightprofiles, et cetera. The expertise level of the persons related to thetext may be based on the entity extraction.

Furthermore, relationship data may comprise the number of historicactions, familiarity between the sender and the receiver, a level of howmuch the two have communicated above the given topic(s), entity analysisdata to determine whether the receiver has the same knowledge, whetherthe message is “matching”, and other factors.

Additionally, a matching value may comprise values varying between 0%and 100% for each individual communication calculated based onhistorical back-and-fourth communication. Hence, all availablecommunication and data channels expressing a relation between twocommunication partners as well as context information may be used totailor and optimize a newly drafted message without changing the intent.

FIG. 3 shows a flowchart diagram illustrating the above-mentionedpre-processing method 300 from another perspective. Historiccommunication data is analyzed, processed, and stored in the knowledgegraph at operation 302. This analysis, processing, and storage in theknowledge graph, at operation 302, is equivalent to semanticallyanalyzing communication history, at operation 102, and forming aknowledge graph, at operation 104, in FIG. 1 . The knowledge graphcomprises notes, or vertices, and relations, or edges, which also mayhave labels and properties. The knowledge graph is especially helpful torepresent relationship information between the respective sender and thereceiver; this may be shown as a sender/receiver pair or a plurality ofsender/receiver pairs. In addition, entities may be added withtimestamps to see when communication happened related to certain topics.The knowledge graph may also include general information about the userswhich could be obtained from social networks or other informationsources. This general information may include preferences, likes,connections to other users, and other data. In another embodiment, theknowledge graph may also include back-and-forth communication betweenusers; an example is illustrated in FIG. 4 below.

The communication together with information from the knowledge graph isthen processed to provide records for the second machine learning modelat operation 304. The second machine learning model may be a supervisedlearning model. Each record of the supervised machine learning model maycome from a message sent from a sender to a receiver.

The formality levels can be categorized by numerically scoring theformality. In a five-tier gradient series, a “1” may refer toprofessional, a “3” may indicate a semi-professional engagement, and a“5” may refer to a completely informal level of conversation. Theconversation levels are trained based on a bag of words algorithm in thelanguage the users are using. A baseline dictionary may be used in a bagof words algorithm. A formal and informal baseline dictionary can bestored in a data storage or database to begin with. An example of such abaseline is shown in the following table to give an idea of the concept:

TABLE 1 Formal Informal How do you do? What's up? If you wouldn't mind,I would like to Can you change . . . request a change. This is a mostwonderful idea, my Great idea, buddy! friend. We concur, let us moveforward. We agree, let's do it.

A personalized or customized set of words may be added to the bag ofwords to categorize them in a scoring pair. Each pair may be scored. Inaccordance with a five-tier gradient, the pairs may be scored from oneto five. Once the classification is completed on the training set, aGaussian Naïve Bayes (GaussianNB) classifier may be used to classify theformality level and derive the strategy for a response in an appropriatefashion.

Once the classifier has provided the formality scores, the scores arestored in the knowledge graph, at operation 306, as dependent attributesin the form of a key value pair {“user”, “formality score”}. The keyvalue pair is stored in the knowledge graph in the form of a decisiontree and the matching value provides a score indicating the impact apast communication interaction had at operation 306.

As a part of further personalization, a bag of words with GaussianNBclassifier may be used. The reply of a message may be analyzed to derivea formality score and store it in the same structure as an appendedvalue. Further, metadata information may be stored in the user'sdictionary and appended to the knowledge graph. Impact may be defined inthe form of an output graph through a combination of factors. Impactfactors may include presence or absence of a reply, timing of a reply,timing of a reply in relation to other replies by the same recipients,timing of a reply in relation to replies by different recipients, timingof a reply in relation to time zones and calendars, presence or absenceof out-of-office messages, length of a reply, content of a reply, areply confirming a task requested by the sender that is performed by thereceiver, and other factors. Note that the reply may also include emojireactions, likes, and other informal communicative indications byrecipients.

Once the system has the information about a partial intent of a user'spartial sentence and detects a level of formality score from a previoussection of communication, the information may be fed into abi-directional transformer BERT model. The information may then be usedto train the proposed underlying system based on partial sentenceconstructions and mask pieces of information in between the phrases. Thepartial sentence constructions and mask pieces of information may thenbe trained using a level of engagement algorithm.

As part of the training step, masked information is hidden during thetraining phase and the hidden information is used as an output to bepredicted. Output predictions are based on previous communication logs,levels of engagement, and other data. These predictions may train atransformer to predict the intent based on partial utterances bypredicting the masked information. The predictive insight information isprovided to a RL model to fine tune it based on engagement feedback.

Additional details for the BERT model implementation comprise also:

-   -   1. {“user”:” engagement score (1-5 which can be normalized using        a standardization interval of [0,1)”} leads to the information        obtained from “Bag of Words” with NB-Classifier.    -   2. json payload is used to append other metrics from the        interaction 1 in the form of list to the script, e.g.:        -   a. {User: engagement_score, additional_payload: [recipient,            timestamp, calendar_context (previous/next meeting)]        -   b. Log file analysis from previous step is appended to            payload as additional metadata as well.    -   3. The above payload is fed as a scoring metric. It is extracted        from payload as weighted vector: e.g., new payload to be fed to        BERT involves: {User1_sentence, Recipient_exchange,        overall_scoring_metric:        sigmoid(w0*engagement_score+w1*timestamp+w2*calendar+w3*meeting)    -   4. BERT model may take Sentence1 and Sentence2. Sentence1 and        Sentence2 may be a combination of information exchanges. (It can        be a series of logs as well, but for simplicity, here only the        past exchange of conversation is considered.) It may be appended        into the categorical input vector as follows:        -   a. {Sentence1 AND Sentence2; Overall_scoring_metric}            -   i. Now, Sentence1 is separately added into the BERT                model. When trained, the input sequence goes through the                transformer model.            -   ii. Words which indicated high score metric are masked                during token embedding phase using a masked feature                vector. A high score metric may be, for example, those                with formality level >0.7.            -   iii. If the masked feature vector is removed based on                previous logs, the used code scrapes the engagement                level to see the score change. The scores compared for a                change value may be, for example, scores with and                without those critical pieces. Various coding languages                may be used; for example, Python code may be used to                scrape the engagement level to identify a score change.            -   iv. Different pieces of information are masked within                the sentence. This feeds back to bag of words to output                a score probability.            -   v. The iteration repeats. The repeat analysis feeds into                the RL model in the form of a numerical feature vector                as a state input for a given user.            -   vi. RL model fine tunes the response and again goes back                to the BERT model in the form of feedback to provide                more information of what information or piece of snippet                can be added to a given masked piece to create more                impact.            -   vii. Identifying a response from a recipient and                extracting sentiment using natural language processing                (NLP) is an enabling art which is fed back into RL loop                as the state vector. If detecting positive feedback, the                reward function does not poll the BERT model to perform                another masking and prediction operation. If the reward                function detects a negative impact factor value, the                BERT model is applied again for a new iteration.            -   viii. As one iterates though the sentence and masking                certain words and/or phrases, the BERT model is now used                in direct fashion to predict the Sentence2 to maximize                the impact factor.    -   5. The adjusted messages are scored. The best version may be        used with the penalty and/or reward for the next iteration until        the quality meets a standard or expectation. The penalty and/or        reward may be, or may be required to meet or exceed, a match        value.

In another implementation, the calculation of the impact a message mayalso take into account sensor and computer vision data. The calculationof the impact of a message may be expressed by a matching value. Such acalculation may be done by dividing the existing methods for learners'engagement detection into categories based on the strategy and type ofinvolvement of the user in the engagement detection process. Three maincategory types which may be used are automatic, semi-automatic, andmanual.

Methods related to engagement tracing may be categorized assemi-automatic in taxonomy. Methods in an automatic category may bedivided into computer vision based methods, sensor data analysis, andlog-file analysis depending on the information that each methodprocesses for engagement detection.

These steps are typically required to bring the method and a systemusing the method into production. Afterward an initial iteration, thesteps may regularly be repeated to re-train the system.

Regarding the reinforcement learning system, the aim of the RL is totailor the message so that it has more impact. Each iteration of the RLneeds at least one scoring with the pre-trained supervised ML model todetermine whether or not there has been any impact.

The state of the RL agent may be determined based on all of theinformation of the message, the metadata, the sender, the receiver, therelationship between sender and receiver, analysis results, andknowledge graph. However, it has only a predicted impact of the message“match value” as its penalty and/or reward. Thus, the system adapts tolearn how to behave and is also tailored to the specific content andpeople involved.

The RL can interactively manipulate the message with the followingtechniques:

-   -   use word replacement, sentence structure spinning, content        reordering, and content deletion,    -   re-shuffle the order of the message blocks,    -   use synonyms,    -   include words or phrases used by the receiver in a past        communication,    -   adjust the style which may be guided with the personality        insights profile from the receiver,    -   use a GPT2 Transformer,    -   and more.

The adjusted messages are scored. The best version may be used with apenalty and/or reward for the next iteration until the quality meets astandard. The penalty and/or reward may be, or may need to meet orexceed, a set match value.

The following extension and enhancements may also be implemented.

Colors may be used to show the sender that the message is not yetoptimal. Colors may also direct input and/or adjustments from thesender. Color indicators may include, for example, green, amber, and/orred to indicate go, caution, and stop, respectively.

A system may recognize if a message does not have enough explanation forthe receiver. The system may add context or definitions to compensate.Context and definitions may come from external sources. For example,information may be imported directly from a website.

A model like the IBM Debater/Speech by Crowd may be used to raisequestions the recipient might have in order to optimize thecommunication; such a model may help answer questions before they areraised to prevent back and forth communication.

The system can make use of a GAN-like (Generative Adversarial Network)setup of two competing models to recreate the text; the differentiatorwould be the model scoring (green/amber/red), the generator would createmodified versions of the email.

In another embodiment, the method can be implemented to extend andcomplete part of a text instead of starting with a complete message andcreating different versions.

In one embodiment, a real-time analysis of communication may beperformed to advise how to get a certain message across throughadditional means other than the content. For example, other means mayinclude using certain tonal cues and/or non-verbal communication.Real-time analysis of communication may include conversations via email,over instant messaging platforms, via phone, and/or face-to-face. Advicemay be offered in a virtual reality setting.

FIG. 4 shows an exemplary diagram of a portion of a knowledge graph 400.Knowledge graph 400 represents a sample conversation with explainablereasoning. Knowledge graph 400 reflects a conversation flow on anaugmented knowledge graph. One of the communication partners may havestated: “I prefer the ending in ‘Film’ directed by Director.” Hiscommunication partner may have answered: “Three movie attemptsessentially ruined the Film; ‘Second Film’ was far more original.” Asdepicted, the knowledge graph 400 may analyze the communication antidentify the Film has an Actor as a “star,” however, the knowledge graph400 may realize the Actor does not lead to a logical (e.g., in the senseof a neural system) cross between the Film and the Second Film, so theknowledge graph 400 may identify the plot of the Film as includingRobots. In some embodiments, the knowledge graph 400 may simultaneouslyanalyzing the meaning behind Second Film and identify that Second Filmwas adapted from a Novel, whose author was a Writer. The knowledge graph400 may realize such a chain does not lead to a logical cross andfurther realize there are comments (e.g., website synopsizes, reviews,etc.) relating to the Second Film that identify Artificial Intelligencein Film 2. The knowledge graph 400 may identify the logical crossconnecting the Film to the Second Film is that Robots and ArtificialIntelligence generally relate.

FIG. 5 shows a diagram of options 500 which may be used during theanalysis phase for the engagement detection 502 in order to develop theknowledge graph 400. It may, for example, be performed in an automatic,semi-automatic, and/or manual way. The lower branches in the tree-likestructure show the technical option of technologies to be implemented.For example, the computer-vision based approach may require a pluralityof sophisticated sensors; such sophisticated sensors may be opticalsensors such as cameras.

FIG. 6 shows a block diagram of components of the message personalizingsystem 600 for personalizing a message between a sender and a receiver.Message personalizing system 600 comprises first analysis means adaptedfor semantically analysing a communication history between the senderand the receiver. The first analysis means is also adapted for forming aknowledge graph between a sender identifier identifying the sender and areceiver identifier identifying the receiver. As depicted, the firstanalysis means is a first analysis unit 602.

Message personalizing system 600 also comprises deriving means adaptedfor deriving from the knowledge graph formality level values between thesender and the receiver. As depicted, deriving means is deriving unit604. Message personalizing system 600 uses a first trainedmachine-learning model and second analysis means adapted for analysingparameter values of replies in the communication history to determinereceiver impact score values. As depicted, the second analysis means issecond analysis unit 606.

Furthermore, message personalizing system 600 comprises a training meansadapted for training a second machine-learning system to generate amodel to predict the receiver impact score value based on the knowledgegraph and the formality level. As depicted, the training means istraining module 608. Message personalizing system 600 also includes aselection means adapted for selecting a linguistic expression in amessage being drafted. As depicted, the selection means is selectionmodule 610. Message personalizing system 600 further includes adetermination means adapted for determining an expression intent of theselected linguistic expression. As depicted, the determination means isa determination module 612. Message personalizing system 600 alsoincludes a modification means adapted for modifying the linguisticexpression based on the formality level and the expression intent whichthereby generates a modified linguistic expression. As depicted, themodification means is modification module 614.

Moreover, message personalizing system 600 comprises a test meansadapted for testing whether the modified linguistic expression has anincreased likelihood to lead to a higher receiver impact score valueusing a third trained machine-learning model. As depicted, the testmeans is testing unit 616. Message personalizing system 600 alsoincludes a repetition means adapted for triggering selection means, thedetermination means, modification means, and the test means until a stopcriterion is met. As depicted, the repetition means is a repetitiontrigger unit 618.

The first analysis unit 602, deriving unit 604, second analysis unit606, training module 608, selection module 610, determination module612, modification module 614, testing unit 616, and repetition triggerunit 618 are communicatively coupled to exchange signals and messages.Alternatively, to a 1:1 connection schema or a message personalizingsystem 600 internal bus system 620 can be used for the signals andmessage transfer between the units and modules.

Embodiments of the disclosure may be implemented together with virtuallyany type of computer, regardless of platform suitability for storingand/or executing program code. FIG. 7 shows, as an example, a computingsystem 700 suitable for executing program code related to the proposedmethod. Computing system 700 may be, for example, a server.

Computing system 700 is only one example of a suitable computer systemand is not intended to suggest any limitation as to the scope of use orfunctionality of embodiments of the disclosure described herein. Incomputer system 700, there are components which are operational withnumerous other general purposes or special purpose computing systemenvironments or configurations. Examples of well-known computingsystems, environments, and/or configurations that may be suitable foruse with computer system 700 include, but are not limited to, personalcomputer systems, server computer systems, thin clients, thick clients,hand-held devices, laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputer systems, mainframe computersystems, distributed cloud computing environments that include any ofthe above systems or devices, and the like. Computer system 700 may bedescribed in the general context of computer system-executableinstructions such as program modules. Such computer system-executableinstructions are able to be executed by computer system 700. Generally,program modules may include routines, programs, objects, components,logic, data structures, and so on. Such modules perform particular tasksor implement particular abstract data types. Computer system 700 may bepracticed in distributed cloud computing environments where tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed cloud computing environment,program modules may be located in both local and remote computer systemstorage media, including memory storage devices.

Computer system/server 700 is shown in the form of a general-purposecomputing device. The components of computer system 700 may include, butare not limited to, one or more processors or processing units 702, asystem memory 704, and a bus 706 that couple various system componentsto a processor 702. Bus 706 represents one or more of any of severaltypes of bus structures including a memory bus, a memory controller, aperipheral bus, an accelerated graphics port, a processor, and a localbus using any of a variety of bus architectures. By way of example, sucharchitectures may include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus. Computer system/server 700 typicallyincludes a variety of computer system readable media. Such media may beany available media that is accessible by computer system 700. Suchmedia includes both volatile and non-volatile media as well as bothremovable and non-removable media.

System memory 704 may include computer system readable media in the formof volatile memory, such as random access memory (RAM) 708 and/or cachememory 710. Computer system 700 may further include other removableand/or non-removable, volatile and/or non-volatile computer systemstorage media. By way of example only, a storage system 712 may beprovided for reading from and writing to a non-removable, non-volatilemagnetic media (not shown). Such a non-removable, non-volatile magneticmedia is typically called a hard drive. Although not shown, a magneticdisk drive for reading from and writing to a removable, non-volatilemagnetic disk and/or an optical disk drive for reading from or writingto a removable, non-volatile optical disk and/or other optical media maybe provided. An example of a removable, non-volatile magnetic disk is afloppy disk. Examples of removable, non-volatile optical disks are aCD-ROM and a DVD-ROM. In such instances, each can be connected to bus706 by one or more data media interfaces. As will be further depictedand described below, memory 704 may include at least one program producthaving at least one program module that configured to carry out thefunctions of embodiments of the disclosure. The one or more programmodules may be described as being a set, regardless of whether asingular module or plural modules.

The program/utility has a program module set 716. The program/utilitymay be stored in memory 704. An operating system, one or moreapplication programs, other program modules, and program data may alsobe stored in memory 704. Each of the operating systems, one or moreapplication programs, other program modules, and program data, or somecombination thereof may include an implementation of a networkingenvironment. Program module set 716 generally carries out the functionsand/or methodologies of embodiments of the disclosure, as describedherein.

Computer system 700 may also communicate with one or more externaldevices 718 such as a keyboard, a pointing device, a display 720, andother computer peripherals. Computer system 700 may also communicatewith one or more devices that may enable a user to interact withcomputer system 700. Computer system 700 may also communicate with anydevices that enable computer system 700 to communicate with one or moreother computing devices; such devices may include a network card, amodem, and other telecommunication devices and/or components. Suchcommunication can occur via Input/Output (I/O) interfaces 714. Computersystem 700 may also communicate with one or more networks such as alocal area network (LAN), a general wide area network (WAN), and/or apublic network via network adapter 722. A public network may include,for example, the Internet. As depicted, network adapter 722 maycommunicate with other components of computer system 700 via bus 706.Although not shown, other hardware and/or software components may beused in conjunction with computer system 700. Examples of other hardwareand/or software which may be used with computer system 700 include, butare not limited to: microcode, device drivers, redundant processingunits, external disk drive arrays, RAID systems, tape drives, and dataarchival storage systems.

Additionally, message personalizing system 600 for personalizing amessage between a sender and a receiver may be attached to the bussystem 706.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 8 , illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 8 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 9 , a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 8 ) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 9 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and communication content tailoring 96.

The descriptions of the various embodiments of the present disclosurehave been presented for purposes of illustration. The descriptions arenot intended to be exhaustive or limited to the embodiments disclosed.Many modifications and variations will be apparent to those of ordinaryskills in the art without departing from the scope and spirit of thedescribed embodiments. The terminology used herein was chosen to bestexplain the principles of the embodiments, the practical application, ortechnical improvement over technologies found in the marketplace, or toenable others of ordinary skills in the art to understand theembodiments disclosed herein.

The present disclosure may be embodied as a system, a method, and/or acomputer program product. The computer program product may include acomputer readable storage medium or media having computer readableprogram instructions thereon for causing a processor to carry outaspects of the present disclosure.

The medium may be an electronic, magnetic, optical, electromagnetic,infrared, or a semi-conductor system for a propagation medium. Examplesof a computer-readable medium may include a semi-conductor orsolid-state memory, magnetic tape, a removable computer diskette, arandom access memory (RAM), a read-only memory (ROM), a rigid magneticdisk, and an optical disk. Current examples of optical disks includecompact disk-read only memory (CD-ROM), compact disk-read/write(CD-R/W), DVD, and Blu-Ray disk.

A computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, anelectronic storage device, a magnetic storage device, an optical storagedevice, an electromagnetic storage device, a semiconductor storagedevice, or any suitable combination of the foregoing. A non-exhaustivelist of more specific examples of the computer readable storage mediumincludes the following: a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), a static randomaccess memory (SRAM), a portable compact disk read-only memory (CD-ROM),a digital versatile disk (DVD), a memory stick, a floppy disk, amechanically encoded device such as punch-cards or raised structures ina groove having instructions recorded thereon, and any suitablecombination of the foregoing. A computer readable storage medium, asused herein, is not to be construed as being transitory signals per se,such as radio waves or other freely propagating electromagnetic waves,electromagnetic waves propagating through a waveguide, or othertransmission media such as light pulses passing through a fiber-opticcable, or electrical signals transmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network. Examples of networks include the Internet, a localarea network, a wide area network, and/or a wireless network. A networkmay comprise copper transmission cables, optical transmission fibers,wireless transmission, routers, firewalls, switches, gateway computers,and/or edge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, source code, and/or object code. Source code and/orobject code may be written in any combination of one or more programminglanguages, including an object-oriented programming language, such asSmalltalk and C++, and/or conventional procedural programming languages,such as the “C” programming language.

Computer readable program instructions may execute entirely on a user'scomputer, partly on a user's computer as a stand-alone software package,partly on a user's computer and partly on a remote computer, or entirelyon a remote computer/server. In the scenario of execution entirely on aremote computer/server, the remote computer may be connected to theuser's computer through any type of network; alternatively, theconnection may be made to an external computer. Such network optionsinclude a local area network (LAN) and a wide area network (WAN). Forexample, a connection may be established through the Internet using anInternet Service Provider. In some embodiments, electronic circuitryincluding may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry in order to performaspects of the present disclosure. Examples of electronic circuitryinclude, for example, programmable logic circuitry, field-programmablegate arrays (FPGA), and programmable logic arrays (PLA).

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner. A computer readable storagemedium having instructions stored therein comprises an article ofmanufacture including instructions which implement aspects of thefunction and/or act as specified in the flowchart and/or block diagramblock or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatuses, or anotherdevice to cause a series of operational steps to be performed on thecomputer, other programmable apparatus, or other device to produce acomputer implemented process. In such a case, the instructions whichexecute on the computer, other programmable apparatuses, or anotherdevice implement the functions and/or acts specified in the flowchartand/or block diagram block or blocks.

The flowcharts and/or block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowcharts or block diagrams may represent a module, segment, or portionof instructions comprising one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustrations, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or act or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to limit the disclosure. As usedherein, the singular forms “a”, “an,” and “the” are intended to includethe plural forms as well unless the context clearly indicates otherwise.It will further be understood that the terms “comprises” and/or“comprising,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans- or steps-plus-function elements in the claims below are intendedto include any structure, material, or act for performing the functionin combination with other claimed elements, as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description; it is not intended to be exhaustive orlimited to the disclosure in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiments are chosen and described in order to best explain theprinciples of the disclosure and the practical application and to enableothers of ordinary skill in the art to understand the disclosure forvarious embodiments with various modifications as such modifications aresuited to the particular use contemplated.

What is claimed is:
 1. A method for personalizing a message between a sender and a receiver, said method comprising: semantically analyzing a communication history between said sender and said receiver and forming a knowledge graph between a sender identifier identifying said sender and a receiver identifier identifying said receiver; deriving a formality level between said sender and said receiver from said knowledge graph using a first trained machine-learning model; analyzing parameter values of replies in said communication history to determine receiver impact score values; training a second machine-learning system to generate a model to predict said receiver impact score values based on said knowledge graph and said formality level; selecting a linguistic expression in a message being drafted; determining an expression intent of said selected linguistic expression; modifying said linguistic expression based on said formality level and said expression intent to generate a modified linguistic expression; testing whether said modified linguistic expression has an increased likelihood to lead to a higher receiver impact score value using a third trained machine-learning model; and repeating said selecting said linguistic expression, determining said expression intent, modifying said linguistic expression, and testing for said higher receiver impact score until a stop criterion is met.
 2. The method according to claim 1 wherein said third machine-learning model is a reinforcement learning model.
 3. The method according to claim 2 wherein said using of said third machine-learning model also comprises training a bi-directional transformer to predict said expression intent.
 4. The method according to claim 3 wherein said training of said bi-directional transformer further comprises using said communication history, said knowledge graph, said formality level, and receiver impact score values as training data.
 5. The method according to claim 1 wherein: said receiver identifier is a plurality of receiver identifiers used to identify a plurality of users, and said modified linguistic expression is built taking effects of all receiver identifiers into account.
 6. The method according to claim 1 wherein said modification of said linguistic expression is influenced by outcomes of at least one selected out of said formality level analysis, a confidentiality level analysis of said message, a message topic analysis, and a tone analysis.
 7. The method according to claim 1 wherein: said modification of said linguistic expression is performed by at least one selected from the group consisting of a replacement of a word, spinning of sentence structure, reordering of content, deletion of a word, re-shuffle an order of message blocks, use of synonyms, wording which was used by said receiver in past communication, adjustment of style, and a GPT2 transformer transformation, and said GPT2 transformer transformation is inculcated with partial sentence creation to generate a segment of a paragraph in a personalized manner by synthesizing personalized text for a user.
 8. The method according to claim 1 wherein said semantic analysis of said communication history comprises: identifying topics with a latent Dirichlet allocation (LDA) topic model.
 9. The method according to claim 1 wherein said formality level is obtained with a bag-of-words model and said first trained machine-learning model is a Gaussian Naïve Bayes classifier.
 10. The method according to claim 1 wherein said linguistic expression is further refined by predicting a mood or task of said receiver at a time at which said message is set to arrive using IoT sensors or calendar information.
 11. The method according to claim 1 wherein said receiver impact score is influenced by at least one selected out of the group consisting of IoT sensor data, wearable system data, computer vision data, presence/absence of a reply, timing of a reply, timing of said reply in relation to other replies by said receiver or different recipients, timing of said reply in relation to time zones and calendars, presence/absence of out-of-office messages, length of said reply, content of said reply, a task requested by said sender being carried out by said receiver, and an emoji.
 12. The method according to claim 1 wherein said message is a written message or a voice message.
 13. The method according to claim 1 wherein: said analysis of said communication history reveals a lack of said communication history, and a secondary communication history replaces said communication history, wherein said secondary communication history is between said receiver and a secondary sender.
 14. A message personalizing system for personalizing a message between a sender and a receiver, said message personalizing system comprising: a memory; and a processor in communication with the memory, the processor being configured to perform operations comprising: semantically analyzing a communication history between said sender and said receiver and adapted for forming a knowledge graph between a sender identifier identifying said sender and a receiver identifier identifying said receiver; deriving from said knowledge graph a formality level between said sender and said receiver using a first trained machine-learning model; analyzing parameter values of replies in said communication history to determine receiver impact score values; training a second machine-learning system to generate a model to predict said receiver impact score values based on said knowledge graph and said formality level; selecting a linguistic expression in a message being drafted; determining an expression intent of said linguistic expression; modifying said linguistic expression based on said formality level and said expression intent to generate a modified linguistic expression; a testing if said modified linguistic expression has an increased likelihood of a higher receiver impact score value using a third trained machine-learning model; and repeating said selecting said linguistic expression, determining said expression intent, modifying said linguistic expression, and testing for said higher receiver impact score until a stop criterion is met.
 15. The message personalizing system according to claim 14 wherein said third machine-learning model is a reinforcement learning model.
 16. The message personalizing system according to claim 15 wherein said using of said third machine-learning model comprises: training a bi-directional transformer to predict said expression intent.
 17. The message personalizing system according to claim 16 wherein said training of said bi-directional transformer comprises: using said communication history, said knowledge graph, said formality level, and said receiver impact score values as training data.
 18. The message personalizing system according to claim 14 wherein: said receiver identifier is a plurality of receiver identifiers used to identify a plurality of users, and said modification means generates said modified linguistic expression based on said formality level and said expression intent between said sender and said plurality of users.
 19. The message personalizing system according to claim 14 wherein said modification of said linguistic expression is influenced by outcomes of at least one selected from the group consisting of said formality level analysis, a confidentiality level analysis of said message, a message topic analysis, and a tone analysis.
 20. The message personalizing system according to claim 14 wherein: said modification of said linguistic expression is performed by at least one selected from the group consisting of a replacement of a word, spinning of sentence structure, reordering of content, deletion of a word, re-shuffle an order of message blocks, use of synonyms, wording which was used by said receiver in past communication, adjustment of style, and a GPT2 transformer transformation, and said GPT2 transformer transformation is inculcated with partial sentence creation to generate a segment of said paragraph in a personalized manner by synthesizing personalized text for a user.
 21. The message personalizing system according to claim 14 wherein said semantic analysis of said communication history comprises identifying topics with a latent Dirichlet allocation (LDA) topic model.
 22. The message personalizing system according to claim 14 wherein said formality level is obtained with a bag-of-words model and said first trained machine-learning model is a Gaussian Naïve Bayes classifier.
 23. The message personalizing system according to claim 14 wherein said linguistic expression is further refined by predicting a mood or a task of said receiver at a time at which said message is set to arrive using IoT sensors or calendar information.
 24. The message personalizing system according to claim 14 wherein said receiver impact score is influenced by at least one selected from the group consisting of IoT sensor data, wearable system data, computer vision data, presence/absence of a reply, timing of a reply, timing of a reply in relation to other replies by said receiver or different recipients, timing of a reply in relation to time zones and calendars, presence/absence of out-of-office messages, length of said reply, content of said reply, a task requested by said sender being carried out by said receiver, and an emoji.
 25. A computer program product for personalizing a message between a sender and a receiver, said computer program product comprising a computer readable storage medium having program instructions embodied therewith, said program instructions being executable by one or more computing systems or controllers to cause said one or more computing systems to: analyze, semantically, a communication history between said sender and said receiver to generate an analysis wherein said analysis is adapted for forming a knowledge graph between a sender identifier identifying said sender and a receiver identifier identifying said receiver, derive from said knowledge graph a formality level between said sender and said receiver using a first trained machine-learning model, analyze parameter values of replies in said communication history to determine receiver impact score values, train a second machine-learning system to generate a model to predict said receiver impact score value based on said knowledge graph and said formality level, select a linguistic expression in a message being drafted, determine an expression intent of said linguistic expression, generate a modified linguistic expression by modifying said linguistic expression based on said formality level and said expression intent to, test whether said modified linguistic expression has an increased likelihood of a higher receiver impact score value by using a third trained machine-learning model, and repeat said program instruction of said selection, said determination, said generation, and said test until a stop criterion is met. 